import pandas as pd
import seaborn as sns
import plotly.express as px
import numpy as np
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
info = stocks['GOOG']
date = stocks['date']
fig, ax = plt.subplots(figsize=(12,9))
ax.plot(date, info)
ax.set_xticks(np.arange(0, len(date)+1, 14)) #length of 105, so to show 8, steps of 14 needs to be taken
ax.set_title('Google stock')
ax.set_ylabel('stock value')
ax.set_xlabel('date')
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
stocks.plot(x = 'date', xticks = (np.arange(0, len(stocks['date'])+1, 14)),
figsize = (12,9), title = 'Stocks', ylabel = 'stock value')
<AxesSubplot:title={'center':'Stocks'}, xlabel='date', ylabel='stock value'>
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
#Question 1
print('Are there differences between male and female when it comes to giving tips?')
sns.scatterplot(x = 'total_bill', y = 'tip', data = tips, hue = 'sex');
Are there differences between male and female when it comes to giving tips?
# fig = plt.figure(figsize=(28,9))
# # g1 = sns.FacetGrid(tips, row = 'total_bill', col='tip', hue='sex')
# # g1.map(sns.scatterplot, 'total_bill', 'tip')
# # g1.add_legend()
# g1 = sns.FacetGrid(tips, col='size', hue='sex')
# g1.map(sns.scatterplot, 'total_bill', 'tip')
# g1.add_legend()
# g1 = sns.FacetGrid(tips, col='time', hue='sex')
# g1.map(sns.scatterplot, 'total_bill', 'tip')
# g1.add_legend()
#fig1 = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="smoker", facet_row="time")
Redo the above exercises (questions 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
# print(stocks.columns)
# list1 = stocks['GOOG']
fig = px.line(stocks, x = 'date', y = stocks.columns, title = 'Stocks', markers = True)
fig.update_traces(mode = 'lines+markers')
fig.show()
#Idon't know how to get different symbols at each line
fig1 = px.scatter(tips, x="total_bill", y="tip", color="sex", facet_col="smoker", facet_row="time")
fig1.show(figsize = (12,9))
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
# YOUR CODE HERE
df2007 = df.query('year == 2007')
new = df2007.groupby('continent').sum()
fig = px.bar(new, x = 'pop', y = new.index, color = new.index,
orientation = 'h', text_auto = '.2s')
fig.update_yaxes(categoryorder = 'total ascending')
fig.update_traces(textposition="outside", showlegend = False)
fig.show()